When optimal is not enough

Currently I work with Professor Mikael Rönnqvist from Université Laval on an inventory and transportation management problem from a forest products company. As a large scale commodity products company, much of its focus is on reducing logistics costs while improving its efficiency and service such as on-time deliveries. As such, we are developing a decision support system prototype that seeks to tackle this planning problem. There is a huge body of scientific literature on vehicle routing, yet we ended up in a very different place than where most papers go. Among the many reasons, one is of particular importance:

They are pretty much uninterested in a solution that simply minimizes their direct transportation cost over a one-month planning horizon.

You read it correctly. The company is interested in keeping costs low, but they also consider that there are many things to take into account beyond the amount of money paid every day to transporters. Among them:

Distribute the workload between the various truck owners and transporters;

Avoid if possible the queuing of trucks in any mill at any given time;

Schedule the delivery of some products to arrive a bit early if possible.

This naturally translates into a certain number of soft constraints which complement the more direct components of costs. The cost structure also incorporates different costing mechanisms such as direct delivery and two types of return trips. Balancing this out is not easy, but it yields interesting discussions with the industrial partners and planners about the value of a solution. (If this aspect of doing optimization interests you, I suggest reading Jean-François Puget’s take on the topic.)

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